Learning multiple local binary descriptors for image matching.
文献类型:期刊论文
作者 | Gao, Yongqian ; Huang, Weilin ; Qiao, Yu |
刊名 | NEUROCOMPUTING
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出版日期 | 2017 |
文献子类 | 期刊论文 |
英文摘要 | Binary descriptors have received extensive research interests due to their low memory storage and computational efficiency. However, the discriminative ability of the binary descriptors is often limited in comparison with general floating point ones. In this paper, we present a learning framework to effectively integrate multiple binary descriptors, which is referred as learning-based multiple binary descriptors (LMBD). We observe that previous successful binary descriptors like Receptive Fields Descriptor (RFD) which includes rectangular pooling area (RFDR) and Gaussian pooling area (RFDG)), BinBoost, and Boosted Gradient Maps (BGM), are highly complementary to each other. We show that the proposed LMBD can improve the discriminative ability of individual binary descriptorssignificantly. We formulate the fusion of multiple groups of the binary descriptors was formulated as a pair-wise ranking problem, which can be solved effectively in a rankSVM framework. Extensive experiments were conducted to evaluate the efficiency of LMBD. The proposed LMBD obtains the error rate of 12.44% on the challenging local patch datasets, which is about 2% lower than the state-of-the-art results (obtained by a learning based floating point descriptor). Furthermore, the proposed binary descriptor also outperforms other binary descriptors on image matching task. |
URL标识 | 查看原文 |
语种 | 英语 |
源URL | [http://ir.siat.ac.cn:8080/handle/172644/11563] ![]() |
专题 | 深圳先进技术研究院_集成所 |
作者单位 | NEUROCOMPUTING |
推荐引用方式 GB/T 7714 | Gao, Yongqian , Huang, Weilin , Qiao, Yu. Learning multiple local binary descriptors for image matching.[J]. NEUROCOMPUTING,2017. |
APA | Gao, Yongqian , Huang, Weilin ,& Qiao, Yu.(2017).Learning multiple local binary descriptors for image matching..NEUROCOMPUTING. |
MLA | Gao, Yongqian ,et al."Learning multiple local binary descriptors for image matching.".NEUROCOMPUTING (2017). |
入库方式: OAI收割
来源:深圳先进技术研究院
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